Table 3 Surveyed works on deep learning and OCC
From: A literature review on one-class classification and its potential applications in big data
# | Paper | Method(s) | Domain |
|---|---|---|---|
1 | Deep learning with Support Vector Data Description [60] | Deep SVDD | UCI datasets |
2 | High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning [61] | DBN-1SVM | Anomaly detection |
3 | Abnormal event detection for video surveillance using deep one-class learning [62] | DOC, SVM, CNN | Video surveillance |
4 | The application of one-class classifier based on CNN in image defect detection [68] | One-Class CNN, CNN | Image processing |
5 | A clustering-based deep autoencoder for one-class image classification [69] | SDAE, Deep Embedded Clustering | Image processing |
6 | Deep one-class classification [70] | Deep SVDD | Image processing object recognition |
7 | Anomaly detection using one-class neural networks [75] | Deep OCC, OCSVM, OCSVDD, IF, KDE | Image processing object recognition |
8 | Deep one-class classification using intra-class splitting [76] | OCSVM-RBF, IF, ImageNet-OCSVM, NN with/without ICS, Deep SVDD | Image processing |
9 | Learning deep features for one-class classification [77] | Deep OCC, Alexnet, VGG16, | Image processing |
10 | Deep embeddings for novelty detection in myopathy [80] | IF, EE, LOF, OCSVM, GANomaly | Healthcare |
11 | Deep multi-sphere support vector data description [82] | DMSVDD | Image processing human activity |
12 | One-class fingerprint presentation attack detection using auto-encoder network [84] | OCPAD, PAD, OCGAM | Image processing attach detection |
13 | Maximum Correntropy criterion-based hierarchical one-class classification [85] | OC-ELM, Parzen, K-means, K-centers, 1-NN, KNN, AE, PCA, MS-OCC, MPM, SCDD, LPDD, SVM, | Image processing |
14 | DSVD‐autoencoder: A scalable distributed privacy‐preserving method for one‐class classification [86] | AUTO-NN, LOF, OCSVM, APE | Miscellaneous datasets Privacy preserving |
15 | DAD: A distributed anomaly detection system using ensemble one-class statistical learning in edge networks [87] | DAD, MCA, TANN, GAA-ADS, ODM, AD-CNN | Network intrusion detection |
16 | G2D: generate to detect anomaly [88] | G2D, GAN, DNN, LPR, R-graph, REAPER, Outlier Pursuit, SSGAN, ALOCC | Image/video processing |